This is a ~50% unstructured sparse variant of the AtomLlama-33K-5x5-DigitMesh model, pruned using the SparseGPT algorithm. Half of the model weights have been set to zero while maintaining digit recognition accuracy through second-order optimization.

Key Features

Base Model: junzzhu/atomllama-33K-5x5-DigitMesh
Sparsity: ~50% (unstructured)
Pruning Method: SparseGPT with Hessian-based importance scoring
Parameters: ~33K total, ~16.5K non-zero
Architecture: LlamaForCausalLM
Task: 5×5 binary digit mesh recognition

Usage

Serving with vLLM

bash

python -m vllm.entrypoints.openai.api_server \
  --model junzzhu/atomllama-33K-5x5-DigitMesh-sparse \
  --max-model-len 32

Example Inference

bash

curl http://localhost:8000/v1/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "junzzhu/atomllama-33K-5x5-DigitMesh-sparse",
    "prompt": "1 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1 1 <SEP>",
    "max_tokens": 1,
    "temperature": 0
  }'

Expected output: D0

Sparsity Details

Type: Unstructured (weights pruned individually based on importance)
Target Sparsity: 50%
Calibration: Pruned using digit pattern activations
Benefits: Reduced memory footprint and potential inference speedup with sparse tensor libraries

License

Apache-2.0

Citation

bibtex

@misc{atomllama-33k-digitMesh-sparse,
  title={AtomLlama-33K-5x5-DigitMesh-Sparse: A 50% Sparse Model for Digit Recognition},
  author={Jun Zhu},
  year={2026},
  howpublished={\url{https://huggingface.co/junzzhu/atomllama-33K-5x5-DigitMesh-sparse}}
}